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Bioinformatics Advance Access published online on April 1, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth238
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received November 5, 2003
Revised January 16, 2004
Accepted February 10, 2004

Applications note

INCA: synonymous codon usage analysis and clustering by means of self-organizing map

Fran Supek 1* Kristian Vlahovicek 2*

1 Department of Molecular Biology, Division of Biology, Faculty of Science, Zagreb University, Rooseveltov trg 6, 10000 Zagreb, Croatia
2 Department of Molecular Biology, Division of Biology, Faculty of Science, Zagreb University, Rooseveltov trg 6, 10000 Zagreb, Croatia; Protein Structure and Bioinformatics, International Centre for Genetic Engineering and Biotechnology, Padriciano 99, 34012 Trieste, Italy

* To whom correspondence should be addressed. E-mail: kristian{at}icgeb.org.


  Abstract

Summary: INCA (INteractive Codon usage Analysis) provides an array of features useful in analysis of synonymous codon usage in whole genomes. In addition to computing codon frequencies and several usage indices, such as ‘codon bias’, effective Nc and CAI, the primary strength of INCA are numerous options for the interactive graphical display of calculated values, thus allowing visual detection of various trends in codon usage. Finally, INCA includes a specific unsupervised neural network algorithm, the self-organizing map (SOM), used for gene clustering according to the preferred utilization of codons.

Availability: INCA is available for the Win32 platform and is free of charge for academic use. For details, visit the web-page http://www.bioinfo-hr.org/inca or contact the author directly.

Supplementary information: Software is accompanied with a user manual and a short tutorial.


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